Hyperspectral cameras image a scene in both spatial and spectral dimensions. Unlike conventional color cameras, which have three broad overlapping color channels over the visible band, hyperspectral cameras have hundreds or thousands of narrow contiguous wavelength channels over the range from visible to long-wave infrared. Hyperspectral cameras may capture images that include millions of pixels, each pixel representing a reflected spectrum.
Hyperspectral imaging has become a core area in the geoscience and remote sensing community. In addition, new applications in object detection, road surface analysis, autonomous navigation, and automatic target recognition are being explored as the size, weight, power requirements, and cost of hyperspectral imaging cameras are reduced.
The spectral demixing problem in hyperspectral data analysis is central to determining the composition of material mixtures based on a reflectivity spectrum. A measured reflectivity spectrum is normally composed of a mixture of spectra arising from different pure materials, or “endmembers”, in the material mixture. Spectral demixing is the process of identifying the endmembers in the material mixture and estimating their abundances.